Suspicious activity recognition for monitoring cheating in exams
نویسندگان
چکیده
Video processing is getting special attention from research and industries. The existence of smart surveillance cameras with high power has opened the for making it conceivable to design intelligent visual systems. Know a day very possible assure invigilators safety during examination period. This work aims distinguish suspicious activities students exam halls. For this, 63 layers deep CNN model suggested named "L4-BranchedActionNet". structure centered on alteration VGG-16 added four blanched. developed framework initially turned into pre-trained by using SoftMax function train CUI-EXAM dataset. dataset detecting activity subsequently sent this algorithm feature extraction. Feature subset optimization applied features that have been obtained. These extracted are first entropy coded, then an ant colony system (ACS) used optimize entropy-based coded features. configured input variety classification models based SVM KNN. With performance 0.9299 in terms accuracy, cubic gets greatest efficiency scores. was further tested CIFAR-100 dataset, shown be accurate tune 0.89796. result indicates frameworks soundness.
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ژورنال
عنوان ژورنال: Proceedings of the Indian National Science Academy. Part A, Physical Sciences
سال: 2022
ISSN: ['0370-0046']
DOI: https://doi.org/10.1007/s43538-022-00069-2